Wednesday 3 March 2021
\(\color{darkgreen}{\text{mathematics}}\) \(\to\)
code \(\to\)
paper \(\to\)
toolthe nature of interdisciplinary research
\(\color{darkred}{\times}\) mathematics \(\not\to\)
\(\color{darkred}{\times}\) code \(\not\to\)
\(\color{darkred}{\times}\) paper \(\not\to\)
\(\color{darkred}{\times}\) tool
An open source license for academics has additional needs:
Recommended by @DrVeronikaCH whose How I Fail blog is well worth checking out
## # A tibble: 6 x 1 ## sections_in_this_talk ## <chr> ## 1 **.Rmd**-driven development ## 2 **functions** ## 3 **documentation** of functions ## 4 **git** and version control ## 5 **testing** of functions and data ## 6 reproducible **document** for publication
| sections |
|---|
.Rmd-driven development |
functions |
documentation of functions |
git and version control |
testing of functions and data |
reproducible document for publication |
Suppose we’re interested in the 5 most frequent observations in farming production systems considered in this 1072, 84` systematic review.
## # A tibble: 3 x 4 ## short_title farmingproducti… farmingproducti… ## <chr> <chr> <chr> ## 1 Aaron (200… <NA> <NA> ## 2 Aavik (200… Livestock <NA> ## 3 Aavik (201… <NA> <NA> ## # … with 1 more variable: ## # farmingproductionsystem_croppedfields <chr>
These data can be analysed and formatted nicely in document.
bufferstrips %>%
select(short_title,
contains("farmingproduction")) %>%
pivot_longer(
cols = contains("farming"),
names_to = "production_system",
values_to = "system_value",
values_drop_na = TRUE
) %>%
separate(col = production_system,
into = c("farming", "system")) %>%
filter(system_value != "Not described",
!str_detect(system_value, "Other")) %>%
count(system) %>%
arrange(desc(n)) %>%
head(5) %>%
gt()
| system | n |
|---|---|
| croppedfields | 738 |
| croppedfieldsinfo | 445 |
| livestock | 199 |
| grassland | 107 |
| livestockinfo | 92 |
| Some Useful Resources | ||||
|---|---|---|---|---|
| .Rmd | ||||
| Item Type | Author | Title | Date | Topic |
| blogPost | Riederer, Emily | 2019-05-04 | .Rmd | |
| book | Grolemund, Yihui Xie, J. J. Allaire, Garrett | 2020 | .Rmd | |
| This is not intended to be a definitive list; these are the resources that helped me the most when I was failing at .Rmd. Share the resources that worked for you via @R-Ladies Freiburg on twitter, or in the chapter's slack group. | ||||
NB: It’s easy for anecdote to become advice, which may not be relevant for everyone. In this workshop, let’s focus on finding affinity in how we all fail at reproducible science. If you have advice or resources, please share by twitter or in the chapter’s slack.
| Some Useful Resources | ||||
|---|---|---|---|---|
| git | ||||
| Item Type | Author | Title | Date | Topic |
| book | Bryan, Jennifer | 2019 | git | |
| journalArticle | Bryan, Jennifer | 2018-01-02 | git | |
| book | various | 2021 | git | |
| book | various | 2021 | git | |
| This is not intended to be a definitive list; these are the resources that helped me the most when I was failing at git. Share the resources that worked for you via @R-Ladies Freiburg on twitter, or in the chapter's slack group. | ||||
If you failed, it means you tried.
You’ll never get everything right in computational science.
Trying is the best you can do. You will fail. You find useful workflows.